Browsing by Author "Pachauri, A."

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Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism
(Elsevier, 2013-05) Pachauri, A.
One of the problems faced in generating test data for branch coverage using a metaheuristic technique is that the population may not contain any individual that encodes test data for which the execution reaches the predicate node of the target branch. In order to deal with this problem, in this paper, we (a) introduce three approaches for ordering branches for selection as targets for coverage with a genetic algorithm (GA) and (b) experimentally evaluate branch ordering together with elitism and memory to improve test data generation performance. An extensive preliminary study was carried out to help frame the research questions and fine tune GA parameters which were then used in the final experimental study.
Comparative Evaluation of A Maximization And Minimization Approach for Test Data Generation with Genetic Algorithm and Binary Particle Swarm Optimization
(International Journal of Software Engineering & Applications, 2012-01) Pachauri, A.
In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation. We use genetic algorithm and binary particle swarm optimization as the search technique and in addition to the usual operators we also employ a branch ordering strategy, memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the branch ordering strategy, memory and elitism.
A Path and Branch Based Approach to Fitness Computation for Program Test Data Generation using Genetic Algorithm
(IEEE, 2015) Pachauri, A.
In this paper we present a novel approach for fitness computation for test data generation using genetic algorithm. Fitness computation is a two-step process. In the first step a target node sequence is determined and in the second step the actual execution path is compared with the target node sequence to compute fitness. Fitness computation uses both branch and path information. Experiments indicate that the described fitness technique results in significant improvement in search performance
Program Test Data Generation for branch coverage with genetic algorithm: comparative evaluation of a maximization and minimization approach” in First International workshop on Software Engineering and Applications
(AIRCJJ, 2012) Pachauri, A.
In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation. We use a genetic algorithm as the search technique and in addition to the usual genetic algorithm operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
Towards a parallel approach for test data generation for branch coverage with genetic algorithm using the extended path prefix strategy
(IEEE, 2015) Pachauri, A.
In this paper we present a proposal for an approach to test data generation for branch coverage with a structured genetic algorithm (GA) using the extended path prefix strategy. The structured GA implements a parallel master-slave distributed model in which each slave implements an elitist panmictic GA. Branches to be covered are selected by the master using the extended path prefix strategy and then dispatched to slaves. The slaves then conduct search for test data to cover the assigned target branch. The extended path prefix strategy ensures that each time a branch is selected for coverage, the sibling branch is already covered and that individuals are available that traverse the sibling. The strategy also permits a variable number of slaves to be used which can help speed up the test data generation process. Experiments on two programs with real inputs indicate that significant improvements are achieved over a simple panmictic GA in terms of number of generations and the coverage achieved.
Use of Clonal Selection Algorithm as Software Test Data Generation Technique
(IEEE, 2012) Pachauri, A.
Clonal selection algorithm is an algorithm that belongs to the class of immune algorithm inspired form clonal selection principle of biological immune system. Initially clonal selection algorithm was designed for machine learning approach and was used in pattern recognition process of artificial intelligence. The other implementation of clonal selection algorithm is in the field of function optimization, which had gained a tremendous attention of the researchers. We too had used the clonal selection algorithm for the software test data generation technique for branch coverage with some modification according to the requirement.